Spatial analysis of trace elements in a moss bio-monitoring data over France by accounting for source, protocol and environmental parameters

Lequy, Émeline; Saby, Nicolas P.A.; Ilyin, Ilia; Bourin, Aude; Sauvage, S.; Leblond, Sébastien

doi:10.1016/j.scitotenv.2017.02.240

Cited by 33 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The moss species in all models has a very high (Cd, Cu, Ni, Pb, Zn and N) or high variable meaning (Hg). This confirms the results of earlier multivariate static analyses [12,18,19,23,40]. It is important to emphasize, however, that the boundary conditions uncovered in these investigations, under which the moss species have different substance contents, can themselves vary spatially.…”

Section: Moss Speciessupporting

confidence: 87%

Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings

Nickel

Schröder

2019

Environ Sci Eur

View full text Add to dashboard Cite

Background: The aim of the study was a statistical evaluation of the statistical relevance of potentially explanatory variables (atmospheric deposition, meteorology, geology, soil, topography, sampling, vegetation structure, land-use density, population density, potential emission sources) correlated with the content of 12 heavy metals and nitrogen in mosses collected from 400 sites across Germany in 2015. Beyond correlation analysis, regression analysis was performed using two methods: random forest regression and multiple linear regression in connection with commonality analysis. Results: The strongest predictor for the content of Cd, Cu, Ni, Pb, Zn and N in mosses was the sampled species. In 2015, the atmospheric deposition showed a lower predictive power compared to earlier campaigns. The mean precipitation (2013-2015) is a significant factor influencing the content of Cd, Pb and Zn in moss samples. Altitude (Cu, Hg and Ni) and slope (Cd) are the strongest topographical predictors. With regard to 14 vegetation structure measures studied, the distance to adjacent tree stands is the strongest predictor (Cd, Cu, Hg, Zn, N), followed by the tree layer height (Cd, Hg, Pb, N), the leaf area index (Cd, N, Zn), and finally the coverage of the tree layer (Ni, Cd, Hg). For forests, the spatial density in radii 100-300 km predominates as significant predictors for Cu, Hg, Ni and N. For the urban areas, there are element-specific different radii between 25 and 300 km (Cd, Cu, Ni, Pb, N) and for agricultural areas usually radii between 50 and 300 km, in which the respective land use is correlated with the element contents. The population density in the 50 and 100 km radius is a variable with high explanatory power for all elements except Hg and N. Conclusions: For Europe-wide analyses, the population density and the proportion of different land-use classes up to 300 km around the moss sampling sites are recommended.

show abstract

Section: Moss Speciessupporting

confidence: 87%

Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings

Nickel

Schröder

2019

Environ Sci Eur

View full text Add to dashboard Cite

show abstract

“…Les mousses captent ainsi efficacement les éléments indispensables à leur croissance, mais capturent aussi simultanément un grand nombre d'éléments qui ne leurs sont pas essentiels, ce qui en fait des organismes de choix pour la biosurveillance de la qualité de l'air [ [18]. Le dispositif BRAMM permet aussi d'identifier des contrastes d'exposition au sein de la France métropolitaine, avec des régions plus ou moins contaminées, comme les bassins industriels et populationnels pour les métaux émis principalement par les activités humaines [19].…”

Section: Les Mousses : Un Organisme Idéal Pour La Biosurveillance De unclassified

Métaux, pollution de l’air et santé

Meyer¹,

Leblond²,

Jacquemin

et al. 2020

Med Sci (Paris)

Self Cite

View full text Add to dashboard Cite

L’exposition à long terme aux particules atmosphériques a des effets bien documentés sur la santé humaine, mais le rôle des métaux reste à explorer. En France, la réglementation impose le suivi des teneurs ambiantes de certains métaux. Mais ces données, du fait d’un maillage trop large ou irrégulier, sont difficilement exploitables en épidémiologie à grande échelle. Les mousses, des végétaux capables d’accumuler les métaux atmosphériques, sont utilisées depuis plusieurs décennies en biosurveillance de la qualité de l’air. Elles fournissent des données exploitables en épidémiologie grâce auxquelles nous avons montré une association entre métaux d’origine anthropique et sur-risque de mortalité en France.

show abstract

“…Soil pollution by potentially toxic elements (PTEs) is one of the most important environmental problems nowadays around the world and it has attracted much public attention [1][2][3][4][5][6][7], especially in countries like China [8][9][10], India [11][12][13], Iran [14][15][16], Pakistan [17][18][19], Brazil [20][21][22], and Bangladesh [23][24][25], which are undergoing industrialization. Among these PTEs, As, Cd, Cu, Ni, and Zn were among the list of most concerned pollutants in soil around the world since they could reside in soils for a long time and could pose a great threat to human health acting via different pathways (e.g., derma contact, inhalation, and ingestion of PTEs-polluted food) [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Xia

Zhu³

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) > Cu (31.34 mg kg−1) > Ni (20.79 mg kg−1) > As (10.65 mg kg−1) > Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) > Cd (R2 = 0.68) > Ni (R2 = 0.67) > Cu (R2 = 0.62) > As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination.

show abstract

Spatial analysis of trace elements in a moss bio-monitoring data over France by accounting for source, protocol and environmental parameters

Cited by 33 publications

References 27 publications

Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings

Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings

Métaux, pollution de l’air et santé

Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods

Contact Info

Product

Resources

About